Dr Paul Yoo
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Overview
Overview
Biography
Prior to his current post at Birkbeck, Paul has held academic and research positions at esteemed institutions such as Cranfield (Defence Academy of the UK), Sydney (USyd) and South Korea (KAIST). He was trained originally as a data scientist with degrees from the University of Sydney, Australia, and has since published over ninety papers in prestigious journals and conferences. He has also been the recipient of over US$ 2.5 million in project funding, and various national and international awards for his work in advanced data analytics, machine learning and secure systems research. These accolades include the IEEE Outstanding Leadership Award, Rozetta Award (formerly CMCRC), Emirates Foundation Research Award, and the ICT Fund Award. Most recently, he was awarded the Samsung award for his research on protecting IoT devices using a machine-learning approach [news], Research England’s Global Challenge Research Fund (GCRF), as well as the Macau Government’s Science and Technology Development Fund (FDCT).
Paul serves as an Associate Editor for several high-ranked journals, including ACM Computing Surveys (Q1), IEEE Transactions on Sustainable Computing (Q1) and IEEE Access (Q1). He has previously served as an Editor for IEEE COMML (Q1) in the areas of big data and machine learning from 2014 to 2019. He is also affiliated with the University of Sydney and Korea Advanced Institute of Science and Technology (KAIST) as a Visiting Professor. Paul is a Senior Member of the IEEE and a Fellow of HEA.
As the Founder and Chair of BIDA's Threat Intelligence Lab, Paul leads a team that aims to bridge the gap between machine learning advancements and the progression of cyber security. The lab is focused on creating the next generation of intelligent cyber defence and security research environment, including applications of cyber physical systems. The lab currently houses 11 PhD students, and recent photos of their lab meetings can be found here.
Paul’s research is centred around the theory and methodology of machine learning for large-scale real-world problems. He has successfully applied various machine learning and big data analytic approaches to diverse problem domains, such as security, biology, finance, manufacturing, communications, and education. His current interests include the application of machine learning and big data technologies in security and defence, finance and the engineering industry (e.g., autonomous vehicle) as well as a range of theoretical and methodological problems.
Paul has also supervised a number of PhD/MSc by Research students to completion, and he welcomes inquiries about research degree (PhD/MPhil) and internship opportunities.
Qualifications
- PhD in Engineering, University of Sydney, Australia, 2009
- PgCert in Academic Practice, Cranfield University, 2019
Web profiles
Administrative responsibilities
- Dy Director (Knowledge Exchange), Birkbeck Institute for Data Analytics (BIDA)
- Chair, Threat Intelligence Lab
- Programme Director, Digital Tech Solutions (Software)
Professional memberships
Senior Member, IEEE
Fellow, HEA
Honours and awards
- Samsung Global Research Outreach Award, Samsung, January 2017
- IEEE Outstanding Leadership Award, IEEE, January 2013
- Rozetta Award, Rozetta Institute, Australia, July 2006
ORCID
0000-0001-7665-8616 -
Supervision and teaching
Supervision and teaching
Supervision
Current doctoral researchers
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ZAID ALMAHMOUD
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ALBERTO MATUOZZO
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DILARA UYSAL
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GASSO MWALUSEKE
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GURMENDER ATWAL
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LAWRENCE OLUSANYA
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NEIL MACKINNON
Doctoral alumni since 2013-14
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SEONGIL HAN
Teaching
Teaching modules
- Foundations of Data Science II (BUCI070H5)
- Applied Machine Learning (BUCI077H7)
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Publications
Publications
Article
- Almahmoud, Zaid and Yoo, Paul and Alhussein, O. and Farhat, I. and Damiani, E. (2023) A holistic and proactive approach to forecasting cyber threats. Scientific Reports 13 (8049), ISSN 2045-2322.
- Matuozzo, Alberto and Yoo, Paul and Provetti, Alessandro (2023) A right kind of wrong: European equity market forecasting with custom feature engineering and loss functions. Expert Systems with Applications ISSN 0957-4174.
- Uysal, Dilara and Yoo, Paul and Kamal, T. (2022) Data-driven malware detection for 6G networks: a survey from the perspective of continuous learning and explainability via visualisation. IEEE Open Journal of Vehicular Technology ISSN 2644-1330.
- Taha, K. and Yoo, Paul and Eddinari, F.Z. (2022) Detecting implicit cross-communities to which an active user belongs. PLoS One ISSN 1932-6203.
- Taha, K. and Yoo, Paul and Eddinari, F.Z. and Nedunkulathil, S. (2022) Inferring the densest multi-profiled cross-community for a user. Knowledge-Based Systems 237 (107681), ISSN 0950-7051.
- Al Hamadi, A. and Yeun, C.Y. and Damiani, E. and Yoo, Paul D. and Hu, J. and Yeun, H.K. and Yim, M.-S. (2021) Explainable Artificial Intelligence to evaluate industrial internal security using EEG signals in IoT Framework. Ad Hoc Networks 123 (102641), ISSN 1570-8705.
- Al Alkeem, E. and Yeun, C.Y. and Yun, J. and Yoo, Paul D. and Chae, M. and Rahman, A. and Asyhari, A.T. (2021) Robust deep identification using ECG and multimodal biometrics for Industrial Internet of Things. Ad Hoc Networks 121 (102581), ISSN 1570-8705.
- Taha, K. and Davuluri, R. and Yoo, Paul D. and Spencer, J. (2021) Personizing the prediction of future susceptibility to a specific disease. PLoS One ISSN 1932-6203.
- Al Hammadi, A. and Lee, D. and Yeun, C.Y. and Damiani, E. and Kim, S.-k. and Yoo, Paul D. and Choi, H.-j. (2020) Novel EEG sensor-based risk framework for the detection of insider threats in safety critical industrial infrastructure. IEEE Access 8, pp. 206222-206234. ISSN 2169-3536.
- Taha, K. and Yoo, Paul D. (2020) An effective disease risk indicator tool. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) ISSN 2694-0604.
- Lee, S.J. and Yoo, Paul D. and Asyhari, T.A. and Jhi, Y. and Chermak, L. and Yeun, C.Y. and Taha, K. (2020) IMPACT: Impersonation attack detection via edge computing using deep autoencoder and feature abstraction. IEEE Access 8, pp. 65520-65529. ISSN 2169-3536.
- Kim, S.-k. and Yeun, C.Y. and Yoo, Paul D. (2019) An enhanced machine learning-based biometric authentication system using RR- Interval Framed Electrocardiograms. IEEE Access 7, pp. 168669 -168674. ISSN 2169-3536.
- Li, M. and Selim, B. and Muhaidat, S. and Sofotasios, P. and Dianati, M. and Yoo, Paul D. and Liang, J. and Wang, A. (2019) Effects of residual hardware impairments on secure NOMA-based cooperative systems. IEEE Access 4, ISSN 2169-3536.
- Al Alkeem, E. and Kim, S.-K. and Yeun, C.Y. and Zemerly, J. and Poon, K. and Yoo, Paul D. (2019) An Enhanced Electrocardiogram biometric authentication system using machine learning. IEEE Access 7, pp. 123069-123075. ISSN 2169-3536.
- Yoo, Paul D. (2019) Popularity-based video caching techniques for cache-enabled networks: a survey. IEEE Access 7, pp. 27699-27719. ISSN 2169-3536.
- Yoo, Paul D. (2019) Censor-based cooperative Multi-Antenna Spectrum Sensing with Imperfect Reporting Channels. IEEE Transactions on Sustainable Computing 5 (1), pp. 48-60. ISSN 2377-3782.
- Yoo, Paul D. (2019) Shortlisting the influential members of criminal organizations and identifying their important communication channels. IEEE Transactions on Information Forensics and Security ISSN 1556-6013.
Book Section
- Kim, S.-K. and Yeun, C.Y. and Yoo, Paul and Lo, N.-W. and Damiani, E. (2022) Deep learning-based arrhythmia detection using RR-Interval framed electrocardiograms. In: The 8th International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems. Springer. (In Press)
- Parker, L. and Yoo, Paul D. and Asyhari, T. and Chermak, L. and Jhi, Y. and Taha, K. (2019) DEMISe: interpretable deep extraction and mutual information selection techniques for IoT intrusion detection. In: ARES '19 Proceedings of the 14th International Conference on Availability, Reliability and Security. ACM. ISBN 9781450371643.
- Yoo, Paul and Zhou, B.B. and Zomaya, A.Y. (2007) Machine intelligence in protein sequence analysis and structure prediction. In: Arabnia, H.R. and Yang, M.Q. and Yang, J.Y. (eds.) BIOCOMP 2007: International Conference on Bioinformatics & Computational Biology. CSREA Press. pp. 370-377.
- Yoo, Paul and Kim, M.H. and Jan, T. (2005) Machine learning techniques and use of event information for stock market prediction: a survey and evaluation. In: CIMCA 2005: International Conference on Computational Intelligence for Modelling Control and Automation. IEEE Computer Society. pp. 835-841. ISBN 9780769525040.
Conference Item
- Matuozzo, Alberto and Yoo, Paul and Provetti, Alessandro and Kim, Maria (2022) Machine learning methods for Equity Time Series forecasting: a compendium. 31st ACM International Conference on Information and Knowledge Management, 2022, Atlanta, U.S.
Editorial
- Yoo, Paul and Tari, Z. (2021) Sustainable information security and forensic computing. IEEE Transactions on Sustainable Computing 6 (1), pp. 2-3. IEEE Computer Society. ISSN 2377-3782.